Dan1elOleary/ai-security-notes

# AI Security Notes This repository contains AI security notes, defensive recommendations, and study documentation focused on modern risks affecting AI-enabled applications and large language model systems. The purpose of this repository is to document key AI security concepts, including prompt injection, retrieval-augmented generation security, secure logging, access control, sensitive information disclosure, and AI supply chain risks. ## Repository Contents | File | Purpose | | ---------------------------- | ----------------------------------------------------------------------------------------------------- | | owasp-llm-top-10-notes.md | Notes covering major OWASP LLM Top 10 security risks and defensive concepts. | | prompt-injection-overview.md | Overview of prompt injection risks, attack patterns, and mitigation strategies. | | rag-security-controls.md | Notes on securing Retrieval-Augmented Generation systems and knowledge base access. | | secure-ai-logging.md | Secure logging practices for AI systems, including redaction and minimization. | | ai-access-control.md | Notes on role-based access control, document-level authorization, and tenant isolation in AI systems. | | ai-supply-chain-risks.md | Overview of risks involving models, datasets, packages, plugins, and third-party AI components. | ## Skills Demonstrated * AI security awareness * OWASP LLM Top 10 concepts * Prompt injection analysis * Retrieval security concepts * Secure logging principles * Access control review * Sensitive information disclosure prevention * AI supply chain risk awareness * Defensive security documentation * Technical writing ## Topics Covered This repository focuses on several important AI security areas: ### Prompt Injection Prompt injection occurs when user-controlled input influences an AI system to ignore intended instructions, reveal restricted information, or perform unauthorized actions. ### Retrieval-Augmented Generation Security RAG systems retrieve information from external knowledge sources before generating a response. These systems require strong access controls to prevent users from retrieving documents they should not be able to access. ### Sensitive Information Disclosure AI systems may expose confidential information if they are connected to internal documents, logs, tickets, databases, or knowledge bases without proper security controls. ### Secure Logging AI logs should avoid storing sensitive information such as credentials, API keys, raw prompts, retrieved confidential context, personal data, and restricted internal information. ### AI Access Control AI systems should enforce access control based on authenticated user identity, role, tenant, permissions, and data classification. Access decisions should be handled by the application or backend system, not by prompt instructions. ### AI Supply Chain Risk AI applications often rely on third-party models, datasets, packages, plugins, and repositories. A compromised dependency or untrusted component can affect the security of the entire AI application. ## Defensive Recommendations * Enforce document-level access control before retrieval. * Use role-based or attribute-based access control. * Apply tenant and user-level filtering in RAG systems. * Redact sensitive information from logs. * Avoid storing raw prompts or retrieved confidential context. * Use least privilege for AI agents, tools, and integrations. * Validate third-party models, packages, datasets, and plugins. * Monitor AI systems for abnormal behavior. * Sanitize model output before rendering it in applications. * Treat external content as untrusted input. * Review AI integrations for excessive permissions. ## Real-World Relevance AI security is becoming increasingly important as organizations adopt AI assistants, chatbots, copilots, automation agents, and retrieval-based knowledge systems. These concepts are useful for roles involving: * SOC analysis * Security operations * IT support * Cloud security * Governance, risk, and compliance * Application security * AI security auditing * Identity and access management ## Portfolio Purpose This repository is part of my professional IT and cybersecurity portfolio. It demonstrates my interest in emerging AI security risks and my ability to document practical defensive controls for AI-enabled environments. ## Disclaimer These notes are for educational and portfolio purposes only. They do not include private flags, paid challenge answers, restricted lab solutions, confidential data, or proprietary information. The focus is on security concepts, defensive recommendations, and professional documentation.